Mapping forest change in Borneo in 2000-2006 by a multispectral statistically-based detection technique with SPOT-VEGETATION

Bontemps, Sophie;Defourny, Pierre
(2007) 2007 International Workshop on the Analysis of Multi-Temporal Remote Sensing Images — Location: Leuven, Belgium (18.July.2007)

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This research aims to develop a multispectral, object-oriented and statistically-based change detection method using SPOT-VEGETATION time series. The dataset consists in daily images spanning years 2000 to 2006 and covering Bornean forest ecosystems. Seasonal composites are processed and homogeneous objects are delineated through a multi-temporal segmentation procedure. Each object is defined by a signature describing its spectral behaviour during years to compare. Such signature is formed by reflectance values of analyzed composites and is statistically expressed allowing accounting for temporal correlations existing between and within time series. Each object is compared to an unchanged situation through a distance computation and a Chi-square test. Objects associated to high distances -indicative of change -are identified and associated to a change probability. The algorithm analyses red, NIR, SWIR and NDVI channels independently, resulting in four distinct sets of detections. These results are then combined to keep only objects detected as changed by the four channels. The overall accuracy ranges from 80% to 91% according to the threshold of probability used and the compared years. In terms of change temporal trajectories, we note a sharp increase of changed area in 2003. We also observe that from 2000 to 2006, fire is always the dominant change factor, with 62% to 82% of detections corresponding to burned areas. Our observations confirm that Borneo currently undergoes environmental degradation dynamics.
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Bontemps, S., & Defourny, P. (2007). Mapping forest change in Borneo in 2000-2006 by a multispectral statistically-based detection technique with SPOT-VEGETATION. 2007 International Workshop on the Analysis of Multi-Temporal RemoteSensing Images, p. Z282-Z287. https://hdl.handle.net/2078.5/260945